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Deep Gaussian Process Priors for Bayesian Inverse Problems

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UNQW04 - UQ for inverse problems in complex systems

Co-authors: Matt Dunlop (Caltech), Mark Girolami (Imperial College), Andrew Stuart (Caltech)

Deep Gaussian processes have received a great deal of attention in the last couple of years, due to their ability to model very complex behaviour. In this talk, we present a general framework for constructing deep Gaussian processes, and provide a mathematical argument for why the depth of the processes is in most cases finite. We also present some numerical experiments, where deep Gaussian processes have been employed as prior distributions in Bayesian inverse problems.

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This talk is part of the Isaac Newton Institute Seminar Series series.

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